#load some required libraries
library(RCurl)
library(glmnet)
library(xgboost)
library(h2o)
library(caret)
library(HDeconometrics)
library(varbvs)
custfit.lasso = function(vect)
{
lasso=ic.glmnet(x = vect$x,y=vect$y,family = "gaussian",alpha = 1)
return(list(lasso = lasso,vars = as.integer(lasso$glmnet$beta[,lasso$glmnet$dim[2]]!=0)))
}
#define a custom function 1 to predict
custpredict.lasso = function(infer, x.new)
{
return(predict(infer$lasso$glmnet,newx = x.new,type = "response")[,which(infer$lasso$glmnet$lambda == infer$lasso$lambda)])#
}
#define a custom function 2 to fit
custfit.ridge = function(vect)
{
ridge = ic.glmnet(x = vect$x,y=vect$y,family = "gaussian",alpha = 0)
return(list(ridge = ridge, vars = as.integer(ridge$glmnet$beta[,ridge$glmnet$dim[2]]!=0)))
}
#define a custom function 2 to predict
custpredict.ridge = function(infer, x.new)
{
return(predict(infer$ridge$glmnet,newx = x.new,type = "response")[,which(infer$ridge$glmnet$lambda == infer$ridge$lambda)])
}
vect = NULL
vect$x = as.matrix(data.example[,-1])
vect$y = data.example$M
library(forecast)
library(glmnet)
library(HDeconometrics)
library(BAS)
library(xgboost)
library(varbvs)
custfit.vb = function(vect)
{
vb = varbvs(X = vect$x,y=vect$y,Z=vect$x,family = "gaussian",verbose = FALSE, maxiter = 1000)
vars = as.integer(vb$pip>=0.5)
return(list(vb = vb, vars = vars))
}
#define a custom function 3 to predict
custpredict.vb = function(infer, x.new)
{
return(predict(infer$vb,X = x.new,Z=x.new))
}
#define your working directory, where the data files are stored
workdir=""
setwd("/nr/samba/user/ahu/EMJMCMC2016/supplementaries/BGNLM/newspop/")
data1 = cbind(0,read.csv("winequality-red.csv",header = T,sep = ";"))
names(data1)[1] = "color"
data2 = cbind(1,read.csv("winequality-white.csv",header = T,sep = ";"))
names(data2)[1] = "color"
data.example = rbind(data1,data2)
rm(data1,data2)
gc()
set.seed(040590)
teid = sample.int(size =3000,n = 6497,replace = F)
test = data.example[teid,]
data.example = data.example[-teid,]
sum(test$quality)
train = data.example
vect = NULL
vect$x = as.matrix(data.example[,-13])
vect$y = data.example$quality
gc()
#prepare the data structures for the final results
results=array(0,dim = c(11,100,5))
# h2o initialize
h2o.init(nthreads=-1, max_mem_size = "6G")
h2o.removeAll()
# h2o.random forest
df = as.h2o(data.example)
train1 = h2o.assign(df , "train1.hex")
valid1 = h2o.assign(df , "valid1.hex")
test1 = h2o.assign(as.h2o(test[,-13]), "test1.hex")
features = names(train1)[-13]
for(ii in 1:100)
{
print(paste("iteration ",ii))
#here we are no longer running BGNLM, since BGNLM algorithms are run via other scripts
#for computational efficiency and speed
# capture.output({withRestarts(tryCatch(capture.output({
#run xGboost logloss gblinear
t=system.time({
param = list(objective = "reg:linear",
eval_metric = "rmse",
booster = "gblinear",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
dval=xgb.DMatrix(data = data.matrix(train[,-13]), label = data.matrix(train[,13]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-13]), label = data.matrix(train[,13]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
# Predict
results[3,ii,4]=t[3]
t=system.time({
dtest = xgb.DMatrix(data.matrix(test[,-13]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
results[3,ii,5]=t[3]
#compute and store the performance metrics
results[3,ii,1]= sqrt(mean((out - test$quality)^2))
results[3,ii,2]=mean(abs(out - test$quality))
results[3,ii,3] = cor(out,test$quality)
# xgboost logLik gbtree
t=system.time({
param = list(objective = "reg:linear",
eval_metric = "rmse",
booster = "gbtree",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
dval=xgb.DMatrix(data = data.matrix(train[,-13]), label = data.matrix(train[,13]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-13]), label = data.matrix(train[,13]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
results[4,ii,4]=t[3]
# Predict
system.time({
dtest = xgb.DMatrix(data.matrix(test[,-13]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
#compute and store the performance metrics
#compute and store the performance metrics
results[4,ii,1]= sqrt(mean((out - test$quality)^2))
results[4,ii,2]=mean(abs(out - test$quality))
results[4,ii,3] = cor(out,test$quality)
#GLMNET (elastic networks) # lasso a=1
t=system.time({
infer.lasso = custfit.lasso(vect)
})
results[5,ii,4]=t[3]
#predict
t=system.time({
out = custpredict.lasso(infer.lasso,as.matrix(test[,-13]))
})
results[5,ii,5]=t[3]
#compute and store the performance metrics
results[5,ii,1]= sqrt(mean((out - test$quality)^2))
results[5,ii,2]=mean(abs(out - test$quality))
results[5,ii,3] = cor(out,test$quality)
#ridge a=0
t=system.time({
infer.ridge = custfit.ridge(vect)
})
results[6,ii,4]=t[3]
#predict
t=system.time({
out = custpredict.ridge(infer.ridge,as.matrix(test[,-13]))
})
#compute and store the performance metrics
results[6,ii,1]= sqrt(mean((out - test$quality)^2))
results[6,ii,2]=mean(abs(out - test$quality))
results[6,ii,3] = cor(out,test$quality)
gc()
#h2o naive bayes
t=system.time({
infer.vb = custfit.vb(vect)
})
#predict
results[10,ii,4]=t[3]
t=system.time({
out=custpredict.vb(infer.vb,as.matrix(test[,-13]))
})
results[10,ii,5]=t[3]
#compute and store the performance metrics
results[10,ii,1]= sqrt(mean((out - test$quality)^2))
results[10,ii,2]=mean(abs(out - test$quality))
results[10,ii,3] = cor(out,test$quality)
gc()
t=system.time({
rf1 = h2o.randomForest( stopping_metric = "RMSE",
training_frame = train1,
validation_frame = valid1,
x=features,
y="Age",
model_id = "rf1",
ntrees = 10000,
stopping_rounds = 3,
score_each_iteration = T,
ignore_const_cols = T,
seed = ii)
})
results[7,ii,4]=t[3]
#predict
t=system.time({
out=h2o.predict(rf1,as.h2o(test1))[,1]
})
out=as.data.frame(as.matrix(out))$predict
#compute and store the performance metrics
results[7,ii,1]= sqrt(mean((out - test$quality)^2))
results[7,ii,2] = mean(abs(out - test$quality))
results[7,ii,3] = cor(out,test$quality)
#h2o deeplearning
t=system.time({
neo.dl = h2o.deeplearning(x = features, y = "Age",hidden=c(200,200,200,200,200,200),
distribution = "gaussian",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
#predict
t=system.time({
out=h2o.predict(neo.dl,as.h2o(test1))[,1]
})
results[8,ii,5]=t[3]
out=as.data.frame(as.matrix(out))$predict
results[8,ii,1]= sqrt(mean((out - test$quality)^2))
results[8,ii,2] = mean(abs(out - test$quality))
results[8,ii,3] = cor(out,test$quality)
#h2o glm
t=system.time({
neo.glm = h2o.glm(x = features, y = "Age",
family = "gaussian",
training_frame = train1,
validation_frame = valid1,
#lambda = 0,
#alpha = 0,
lambda_search = F,
seed = ii)
})
#predict
results[9,ii,4]=t[3]
t=system.time({
out=h2o.predict(neo.glm,as.h2o(test1))[,1]
})
results[9,ii,5]=t[3]
out=as.data.frame(as.matrix(out))$predict
results[9,ii,1]= sqrt(mean((out - test$quality)^2))
results[9,ii,2] = mean(abs(out - test$quality))
results[9,ii,3] = cor(out,test$quality)
print( results[,ii,1])
gc()
#})), abort = function(){onerr=TRUE;out=NULL})})
}
ids=1:100
ress=results[,ids,]
#make the joint summary of the runs, including min, max and medians of the performance metrics
summary.results=array(data = NA,dim = c(15,15))
for(i in 3:10)
{
for(j in 1:5)
{
summary.results[i,(j-1)*3+1]=min(ress[i,,j])
summary.results[i,(j-1)*3+2]=median(ress[i,,j])
summary.results[i,(j-1)*3+3]=max(ress[i,,j])
}
}
summary.results=as.data.frame(summary.results)
summary.results = summary.results[3:10,]
names(summary.results)=c("min(rmse)","median(rmse)","max(rmse)","min(mae)","median(mae)","max(mae)","min(corr)","median(corr)","max(cor)","min(ltime)","median(ltime)","max(ltime)","min(ptime)","median(ptime)","max(ptime)")
rownames(summary.results)=c("lXGBOOST(logLik)","tXGBOOST(logLik)","LASSO","RIDGE","RFOREST","DEEPNETS","GR","VARBAYESS")
write.csv(x = summary.results,file = "summarycompete.csv")
#write the final reults into the files
#write.csv(x = train,file = "/mn/sarpanitu/ansatte-u2/aliaksah/abeldata/breast cancer/train.csv")
#write.csv(x = test,file = "/mn/sarpanitu/ansatte-u2/aliaksah/abeldata/breast cancer/test.csv")
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